Optimize Solr Search Results with Machine Learning,
Shopper Behavior and Your Analytics
Online retailers are looking for an efficient way to ensure that shoppers receive the most relevant search results from Solr. They need a solution that enables machine learning to automatically optimize Solr search results but also allow for customization to meet business objectives.
Merchants are also looking to drive uplift beyond popularity from clickstream by leveraging valuable analytics and sales metrics on-hand. A tailored approach is necessary but is often time consuming and costly for many online retailers.
High-quality Solr search results are achieved with influence from shopper engagement behavior along with metrics such as revenue, conversion, units sold, newness, inventory, rating, and sales rank.
FindTuner lets you immediately visualize and take advantage of any data to dynamically rank results, positively influence conversion, and create the perfect mix of products.
AutoTune delivers the best results with no manual effort by learning from shopper’s behavior, purchase history and buying patterns. Using machine learning models, AutoTune drives the most relevant results, provides better shopping experiences, and responds quickly to trends.
AutoTune Algorithms enable merchandisers to optimize Solr search results and dynamically rank products with an easy to use interface that makes tuning and testing algorithms simple.
Easily pass data to FindTuner to execute multivariate tests and find the best strategy for your shoppers.